Sentence examples for defined regularization from inspiring English sources

In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery.

In the objective function, (||f||_{k}^{2}) is the norm of (f), which is associated to a kernel function (k) (described below), and (lambda > 0) is a user defined regularization parameter.

The optimization problem can now be defined as finding the vector x that minimizes the quantity ||Σ x−b||2+λ2 ||x||2, where b is a column vector, ||x|| indicates the norm of x, and λ is a positively defined regularization parameter.

R p is the level set regularization term as defined in [8].

The importance of the regularization is always defined through positive regularization constants the higher the value, the higher the importance.

PEPR method defines the regularization parameter as a function of the projection error developed by difference between experimental measurements and calculated data.

We define a regularization of a twisted covariant system (G, A, τ) as a G-equivariant Continuous map from Prim(A) into a locally compact G-space Ω with certain additional properties.

We define the regularization in time of the function (u_{varepsilon }) by (u_{varepsilon})_{nu} x,t)=nuint_{-infty}^{t} e^{nu(theta -t }bar{u}_{varepsilon } x,theta),mathrm{d}thetaquadmbox{for } nu inmathbb{N}, where (bar{u}_{varepsilon } x,theta)=u_{varepsilon } x,theta)) if (theta>0); (bar{u}_{varepsilon } x,theta)=0) if (thetaleq0).

We define a regularization approximation solution of problem (1.1) for noisy data g δ ( x ) as follows: f δ, ξ max ( x ) : = 1 2 π ∫ − ∞ ∞ e i ξ x ψ α θ 1 − e − ψ α θ g ˆ δ χ max d ξ, (2.1).

In fact, the restoration of the center is a linear process defined by the regularization expression (29), but the boundary reconstruction comes from a nonlinear truncation which requires different performance.

For a non-separable case, the classification problem is generalized by introducing slack variables ξ i and a user-defined regularization parameter C. Then the classification problem is to minimize the following quantity J w = 1 2 w T w + C ∑ i = 1 l ξ i. (5).